A new multivariable grey prediction model with structure compatibility

Zeng, Bo; Duan, Huiming; Zhou, Yufeng

doi:10.1016/j.apm.2019.05.044

Cited by 179 publications

(78 citation statements)

References 24 publications

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“…e gray prediction method, namely, GM (1, 1), is effective in predicting a system containing uncertain factors. It can make high-precision predictions with a small sample size [67]. e main principle of GM (1, 1) is to identify the different degrees of the trends among factors, generate raw data and find the laws, generate regular data sequences, establish differential equation models, and predict the system's trend.…”

Section: Prediction Of the System's Ccdmentioning

confidence: 99%

Analysis and Prediction of the Coupling Coordination Relationship between Tourism and Air Environment: Yangtze River Economic Zone in China as Example

Geng

Wei

Zhang

et al. 2020

Discrete Dynamics in Nature and Society

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The results show that the comprehensive development degree (CDD) of the tourism-air environment system mainly maintains stable with fluctuation and the gap among different reaches in the Zone is declining; the coupling coordination degree’s (CCD) tendency in most regions remains similar as in the previous decade. The results illustrate that the method combing information entropy weight and the technique for order preference by similarity to an ideal solution (IEW-TOPSIS), coupling coordination degree model (CCDM), and gray GM (1, 1) prediction model is effective in evaluating the coupling coordination relationship between the subsystems of tourism and air environment and in proposing specific countermeasures for tourism development and air environment governance.

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Section: Prediction Of the System's Ccdmentioning

confidence: 99%

Analysis and Prediction of the Coupling Coordination Relationship between Tourism and Air Environment: Yangtze River Economic Zone in China as Example

Geng

Wei

Zhang

et al. 2020

Discrete Dynamics in Nature and Society

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show abstract

“…A great deal of e ective prediction models has been proposed in the past few decades. Among numerous existing prediction models, the grey theorybased models, which were rst proposed by Deng in 1982 [2], have attracted many researchers' attention due to its simple form, high computing e ciency, and high prediction accuracy especially for limited data and insu cient information [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…To improve the prediction accuracy of the GM(1,1) model, many researchers have carried out a lot of works from di erent aspects, such as nding new accumulation generating operators [14][15][16][17][18][19], constructing more accurate background value formula [20,21], choosing parameter optimization methods [22], improving initial guess [23], and reducing residuals based on Fourier analysis and Markov chain [9,20]. Recently, some nonhomogeneous, nonlinear, hybrid, and multivariable grey models are proposed, see [6,[24][25][26] for examples. Modeling mechanism analysis can be found in [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

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“…Also, the tunable weighted parameters of NSGM (1,1) are automatically sought out by using the ant lion optimizer [21]. Zeng et al optimized the structure compatibility of a multivariable grey model with adding a random term, a linear term, and a dependent variable lag term [22]. Besides, the fractional-order accumulation is also significantly valid for increasing the prediction capacity of the grey model [23].…”

Section: Introductionmentioning

confidence: 99%

A Novel Power‐Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

Zhang

She

2019

Complexity

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Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power-driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power-driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real-world datasets are conducted, and the results indicate that the power-driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. Finally, the power-driven grey model is applied in forecasting the total residential energy and the thermal energy consumption of China.

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A new multivariable grey prediction model with structure compatibility

Cited by 179 publications

References 24 publications

Analysis and Prediction of the Coupling Coordination Relationship between Tourism and Air Environment: Yangtze River Economic Zone in China as Example

Analysis and Prediction of the Coupling Coordination Relationship between Tourism and Air Environment: Yangtze River Economic Zone in China as Example

A Novel Weighted Fractional GM(1,1) Model and Its Applications

A Novel Power‐Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

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